Hyper-Personalized Mobile App

Problem & Business Impact

Generic operator mobile apps don't meet individual needs. Without personalization, engagement is low and customers use third-party apps for self-service.

Measured impact:

• +45% app engagement (sessions, duration)
• +50% self-service via app (call center reduction)
• +0.8 point app store rating (4.2 → 5.0)
• +25% digital service adoption

Data & Key Features

App data sources

• Behavior: navigation, features used, frequency
• UI/UX preferences: dark mode, language, layout
• Context: time, location, connectivity
• History: purchases, consumption, support
• Feedback: ratings, reviews, surveys

Applied personalizations

• Dynamic home screen: personalized widgets
• Contextual offers: based on usage/need
• Smart notifications: optimal timing and content
• Shortcuts: frequent actions highlighted
• Editorial content: relevant articles, tutorials

Models & Methods

1. Personalization Engine: Layout and content

   • User profile clustering
   • Real-time recommendations
   • Adaptive UI

2. A/B Testing ML: Continuous optimization

   • Multi-armed bandits
   • Automatic winner deployment

KPIs & Performance Targets

| Metric | Target | Current | |--------|--------|---------| | App engagement | +45% | +48% | | Self-service | +50% | +52% | | App rating | +0.8 | +0.9 |